A new computational method has connected several target genes to autism, according to new research.
The findings, along with other recent discoveries, could lead to screening tools for young children—and help doctors choose the best intervention when making a diagnosis.
Autism is a spectrum of closely related symptoms involving behavioral, social, and cognitive deficits. Early detection is key to producing the best outcomes; however, searching for genetic causes is complicated because of the various symptoms found within the spectrum.
“In this study we started with more than 2,591 families who had only one child with autism and neither the parents nor the siblings had been diagnosed with autism,” says Chi-Ren Shyu, professor of electrical engineering and computer science at the University of Missouri and director of the Informatics Institute.
“This created a genetically diverse group composed of an estimated 10 million genetic variants. We narrowed it down to the 30,000 most promising variants, then used preset algorithms and the big data capabilities of our high-performance computing equipment at MU to ‘mine’ those genetic variables.”
The genetic samples were obtained from the Simons Foundation Autism Research Initiative. Researchers collected samples from children with diagnosed cases and their unaffected parents and siblings, leading to more than 11,500 individuals.
Using advanced computational techniques, Shyu and colleagues were able to identify 286 genes they then collected into 12 subgroups that exhibited commonly seen characteristics of children on the spectrum. Of these genes, 193 potentially new genes not found in previous autism studies were discovered.
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“Autism is heterogeneous, meaning that the genetic causes are varied and complex,” says Judith Miles, professor of child health-genetics in the MU Thompson Center for Autism and Neurodevelopmental Disorders. “This complexity makes it tough for geneticists to get at the root of what triggers the development of autism in more conventional ways.
“The methods developed by Dr. Shyu and the results our team identified are giving geneticists a wealth of targets we’d not considered before—by narrowing down the genetic markers, we may be able to develop clinical programs and methods that can help diagnose and treat the disease. These results are a quantum leap forward in the study of the genetic causes of autism.”
The informatics framework is now ready for a much larger scale of research, such as genetic samples to be collected through the Simons Foundation Powering Autism Research for Knowledge (SPARK), the nation’s largest autism study.
SPARK is partnering with scientists who hope to collect information and DNA for genetic analysis from 50,000 individuals with autism—and their families—to advance understanding of causes and hasten the discovery of supports and treatments.
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Anyone interested in learning more about SPARK or in participating in the study can visit: www.SPARKforAutism.org/MUTC, or contact Amanda Shocklee at (573) 884-6092 or shockleea@missouri.edu.
Researchers report their findings in the Journal of Biomedical Informatics. The National Institutes of Health; the Shumaker Endowment for Biomedical Informatics; the National Science Foundation; and the Simons Foundation supported the work.
Source: University of Missouri
